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The researched area overlaps the territory belonging to Crişul Alb and Crişul Negru river basins. The study is based on processing and interpretation data from 33 hydrometric stations of which 18 stations control hydrographic basins with surfaces of less than 150 km2. To highlight the seasonal hydrological flow regime, we took into account three time periods (1950-1967, 1950-2009 and 1970-2009). For all rivers the highest flow values appear during springtime, while the smallest contribution to the multiannual average volume is realized in autumn. The time variation of seasonal flow was highlighted by analysis and variation of extreme values coefficients and also by analysing seasonal flow trends that appeared in all three time periods.

The Maramureş Depression is drained by Vişeu and Iza rivers. Both have a partially influenced regime. The flow in lower Iza Basin is influenced by the Runcu Reservoir and by the Runcu – Valea Neagră derivation that are situated on the upper Mara River. The flow is controlled by three hydrometric stations – Mara and Vadu Izei on Mara River, and Vadu Izei on Iza River. We took into analyse the time period 1993 – 2012 using drained and influenced monthly average discharge data. The variation of monthly and annual deviations for Mara Station shows positive values for each year and for almost each month. The flow influence degree, in absolute and relative values, is evaluated for all three stations. The hydrometric stations are situated into a certain type of influenced flow by using the Q infl. / Q rec. ratio.

Investigated region is overlapping the territory drained by tributaries of Someşul Mic and Arieş River and also the creeks collected by Mureş River between the mouth of Arieş and Geoagiu River. The study is based on processing and interpreting data from 24 gauging stations, of which 18 control surfaces basins below 150 km2. To highlight the features of seasonal flow regime we have considered three periods (1950-1967, 1950-2009 and 1970-2009). Thus, all rivers are recording a dominant flow during spring while the lowest annual average volume is related to winter season. Seasonal time variation of river flow was highlighted by analyzing the trends in the three periods using the variation coefficients.

Geographical teaching has a long tradition in Cluj-Napoca. There are many fields of study at the Faculty of Geography from Cluj-Napoca, which has separated from a larger faculty in 1994, such is Hydrology – Meteorology. It first appeared in 2006, and already has five generations of students. The enquiry made at eight institutions between 1972 and 2013 shows that students’ penetration inside meteorological and hydrological institutions was a good one. A big part of these students come from the Geography field of study, which has a long tradition, and from the new field – Hydro-Meteorology. It can be observed a pronounced increase of employment since 1999. The biggest chances in obtaining a job in this field appear three years after the graduation. Many specialists finished their Geography studies after obtaining the job. Most of our students work as hydrologist or meteorologist, but also in other jobs, a great number belonging to those working in leadership positions. From this enquiry it can be observed that the field of Hydrology – Meteorology was an appropriate one, succeeding to fulfil its purpose.

The present study compared aqueous and ethanolic solvents to evaluate the in vitro anticancer, anti-elastase and anti-collagenase activity of Hypnea pannosa seaweed extracts. Additionally, the study evaluated the extract's preliminary phytochemical composition, total phenolic and flavonoid content, antioxidant and antibacterial activity. The analysis revealed the presence of several phytochemicals and the range of total phenolic (11.14±0.17 mg gallic acid equivalents/g and flavonoid (5.83±0.09 mg quercetin equivalent/g) content in the ethanolic extract. The ethanolic extract showed the highest antioxidant activity against 2,2-diphenyl-1-picrylhydrazyl (half-maximal inhibitory concentration=6.32±0.04 µg/ml) and the most potent anticancer effect against COLO 829 melanoma cancer cell lines (half-maximal inhibitory concentration=95.70 µg/ml). It also exhibited the most potent anti-elastase and anti-collagenase effect against human melanoma cells, with halfmaximal inhibitory concentration values of 48.53 and 48.76 µg/ml, respectively. The ethanol extract showed maximum antibacterial activity and the highest inhibition zone of 14.5 and 14 mm against Escherichia coli and Pseudomonas aeruginosa, respectively. Fourier transform infrared spectroscopic analysis shows that many polyphenols are present in ethanol extract, which has received importance in cosmeceutical perspectives (3925-3893 cm-1). The study hence provides evidence that the ethanol extract of Hypnea pannosa has significant potential for antioxidants, anticancer, antibacterial, anti-elastase and anti-collagenase and may lead to further cosmeceutical applications through the isolation and purification of bioactive compounds. [ABSTRACT FROM AUTHOR]

The rapid growth of Fintech innovation and the widespread adoption of blockchain technologies have indeed had a transformative impact on the financial industry. In this paper, the focus is on the application of machine learning algorithms, specifically the Random Forest Regression algorithm, within the context of Fintech and blockchain. This research contributes to the advancement of machine learning techniques in the field of Fintech and blockchain. The Random Forest Regression algorithm utilizes ensemble learning, combining multiple decision trees to analyze complex financial data and make predictions on various outcomes. This algorithm has proven to be effective in addressing key challenges within the industry, such as predicting loan defaults, detecting fraud, and assessing risks. Through experimental evaluations and case studies, the paper demonstrates the effectiveness of the Random Forest Regression algorithm in enhancing Fintech innovation in blockchain applications. The algorithm improved accuracy, scalability, and interpretability enable financial institutions to make data-driven decisions and optimize their operations. [ABSTRACT FROM AUTHOR]